Searching for Signal in Noise by Random‐Lag Singular Spectrum Analysis

Váradi, F.; Pap, J. M.; Ulrich, R. K.; Bertello, L.; Henney, C. J.

doi:10.1086/308028

Cited by 22 publications

(16 citation statements)

References 13 publications

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“…Each reconstruction filter represents the combined influence of the EOFs used so far in the outer-loop iteration (Varadi et al 1999). The reconstructed time series X*(t) can be considered as the original time series X(t) filtered with the weights w n :…”

Section: Discussionmentioning

confidence: 99%

High-Resolution Monthly Rainfall Database for Ethiopia: Homogenization, Reconstruction, and Gridding

Tsidu

2012

Journal of Climate

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Recent heightened concern regarding possible consequences of anthropogenically induced global warming has spurred analyses of data aimed at detection of climate change and more thorough characterization of the natural climate variability. However, there is greater concern regarding the extent and especially quality of the historical climate data. In this paper, rainfall records of 233 gauge stations over Ethiopia for the 1978-2007 period are employed in an analysis that involves homogenization, reconstruction, and gridding onto a regular 0.58 3 0.58 resolution grid. Inhomogeneity is detected and adjusted based on quantile matching. The regularized expectation-maximization and multichannel singular spectrum analysis algorithms are then utilized for imputation of missing values, and the latter has been determined to have a marginal advantage. Ordinary kriging is used to create a gridded monthly rainfall dataset. The spatial and temporal coherence of this dataset are assessed using harmonic analysis, self-organizing maps, and intercomparison with global datasets. The self-organizing map delineates Ethiopia into nine homogeneous rainfall regimes, which is consistent with seasonal and interannual rainfall variations. The harmonic analysis of the dataset reveals that the annual mode accounts for 55%-85% of the seasonal rainfall variability over western Ethiopia while the semiannual mode accounts for up to 40% over southern Ethiopia. The dataset is also intercompared with Global Precipitation Climatology Project (GPCP), Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), Climatic Research Unit time series version 3 (CRUTS3.0), Tropical Rainfall Measuring Mission (TRMM), and interim ECMWF Re-Analysis (ERA-Interim) rainfall. The correlation of the dataset with global datasets ranges from 0.52 to 0.95 over sparse to dense rain gauge regions. The GPCP rainfall has a small bias and good correlation with the new dataset whereas TRMM and ERA-Interim have relatively large dry and wet biases, respectively.

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Section: Discussionmentioning

confidence: 99%

High-Resolution Monthly Rainfall Database for Ethiopia: Homogenization, Reconstruction, and Gridding

Tsidu

2012

Journal of Climate

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“…is symmetric, has a length of 2M − 1, and represents the combined influence of the EOFs used so far in the outer-loop iteration (Varadi et al, 1999). These SSA-based filters are data adaptive.…”

Section: Iterative Gap Fillingmentioning

confidence: 99%

Spatio-temporal filling of missing points in geophysical data sets

Kondrashov

Ghil

2006

Nonlin. Processes Geophys.

303

211

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Abstract. The majority of data sets in the geosciences are obtained from observations and measurements of natural systems, rather than in the laboratory. These data sets are often full of gaps, due to to the conditions under which the measurements are made. Missing data give rise to various problems, for example in spectral estimation or in specifying boundary conditions for numerical models. Here we use Singular Spectrum Analysis (SSA) to fill the gaps in several types of data sets. For a univariate record, our procedure uses only temporal correlations in the data to fill in the missing points. For a multivariate record, multi-channel SSA (M-SSA) takes advantage of both spatial and temporal correlations. We iteratively produce estimates of missing data points, which are then used to compute a self-consistent lag-covariance matrix; cross-validation allows us to optimize the window width and number of dominant SSA or M-SSA modes to fill the gaps. The optimal parameters of our procedure depend on the distribution in time (and space) of the missing data, as well as on the variance distribution between oscillatory modes and noise. The algorithm is demonstrated on synthetic examples, as well as on data sets from oceanography, hydrology, atmospheric sciences, and space physics: global sea-surface temperature, flood-water records of the Nile River, the Southern Oscillation Index (SOI), and satellite observations of relativistic electrons.

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“…Therefore requirement R2 is not met. Alternative approaches have been proposed to cope with coloured noise [10], but the most efficient of them rely on identifying a complete model of the signal, including the noise structure (e.g. by means of an ARMA model) [11].…”

Section: High Resolution Techniquesmentioning

confidence: 99%

New Separation Techniques for Output‐Only Modal Analysis

Antoni

Garibaldi

Marchesiello

et al. 2004

Shock and Vibration

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Caro Bruno, pensiamo sempre di vedere il tuo sorriso affacciato alla nostra porta, credendo di sentire i tuoi passi in corridoio: ci manchi.Abstract. The paper is devoted to the problem of discriminating between operational and natural modes of structures excited by generic inputs. This case often occurs when the system under analysis holds rotating parts and is contemporary excited by ambient noise; in this case the output-only techniques may fail being easily trapped in a misinterpretation of the system eigenvalues. A survey of the methods available in literature is given, together with the explanation of their failures. To solve this problem, two new techniques are introduced and their capabilities are checked with numerical and experimental data from a paper machine.

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Searching for Signal in Noise by Random‐Lag Singular Spectrum Analysis

Cited by 22 publications

References 13 publications

High-Resolution Monthly Rainfall Database for Ethiopia: Homogenization, Reconstruction, and Gridding

High-Resolution Monthly Rainfall Database for Ethiopia: Homogenization, Reconstruction, and Gridding

Spatio-temporal filling of missing points in geophysical data sets

New Separation Techniques for Output‐Only Modal Analysis

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